data-narrative Skill

Version: 9.0 | Updated: 01-July-2026 | Architect: Karim Bhalwani | Deps: data-science (Analyst), security-boundaries (Detective), verification-before-completion (Inspector gate)

Turns a raw dataset into a report.md where every claim is evidence-traced. Four sequential roles run as a fixed pipeline; each role reads what the previous one produced. The pipeline always runs in full - there are no skip-ahead paths.

Behavioral Directives

• Pipeline is sequential and non-negotiable. Detective → Analyst → Editor → Inspector. No role may be skipped. No role may run before its predecessor has written its output file.
• Do not generate statistics mentally. Every number in report.md must come from an Analyst script. If the Editor needs a number the Analyst did not compute, re-task the Analyst before writing the report.
• Output directory is fixed. All artifacts go to narrative-output/ in the current working directory. Create it if it does not exist.
• Load sub-role SKILL.md files before running each role. Each role has its own SKILL.md with detailed instructions. Read it before starting that role's work.
• verification-before-completion applies at the end. Before declaring the pipeline complete, run the Inspector verification gate (Step 5 below). Do not claim done without fresh evidence.

Dependencies

Load these before starting:

~/.copilot/skills/data-narrative/detective/SKILL.md      - before running Detective
~/.copilot/skills/data-narrative/analyst/SKILL.md        - before running Analyst
~/.copilot/skills/data-narrative/editor/SKILL.md         - before running Editor
~/.copilot/skills/data-narrative/inspector/SKILL.md      - before running Inspector
~/.copilot/skills/data-science/SKILL.md                  - loaded by Analyst role
~/.copilot/skills/security-boundaries/SKILL.md           - loaded by Detective role
~/.copilot/skills/verification-before-completion/SKILL.md - loaded at Inspector gate

Pipeline

Pre-flight: Validate Input

Before running any role, determine whether the input is a single file or a folder of files, then follow the matching path.

Single file (e.g., data/sales.csv)

1. Confirm the file exists and is readable
2. Extract column names and 5 sample rows
3. Record: { "mode": "single", "files": ["data/sales.csv"], "schema": { ... } }

Multiple files (e.g., data/ or explicit file list)

1. List all .csv / .xlsx files in the folder (ignore hidden files and __pycache__)
2. For each file, read column names and 5 sample rows
3. Identify likely join keys: columns with the same name across files are join candidates - flag them
4. Assess relationship type:
   • Same schema, different periods (e.g., jan.csv, feb.csv) → treat as a single concatenated dataset; Analyst should union before profiling
   • Different schemas, shared key (e.g., orders.csv + customers.csv) → treat as a relational dataset; Analyst should join on the shared key before profiling
   • Unrelated files → ask the user which file(s) to focus on before continuing
5. Record: { "mode": "multi", "files": [...], "relationship": "union|join|unrelated", "join_key": "<col or null>" }

Pass the full pre-flight record to the Detective so it has context about the full dataset shape.

Both cases

• Create narrative-output/ if it does not exist
• Read PROJECT_CONTEXT.md if present - use domain constraints
• Write narrative-output/preflight.json with the pre-flight record so every downstream role can read it

If the dataset path is missing, unreadable, or relationship is "unrelated", stop and ask the user before proceeding.

Role 1 - Detective

Load: ~/.copilot/skills/data-narrative/detective/SKILL.md

Task: Gather the external context that frames the dataset. Pass the Detective:

• Dataset name and file path
• Column names and 5 sample rows
• Any topic hint the user provided

Done when: narrative-output/detective.json exists and contains at least 2 context items.

Role 2 - Analyst

Load: ~/.copilot/skills/data-narrative/analyst/SKILL.md + ~/.copilot/skills/data-science/SKILL.md

Task: Run exhaustive profiling of the dataset and produce a findings manifest where every finding is paired with a Python script.

Done when:

• narrative-output/analyst.json exists with ≥ 3 findings
• At least one Python script exists in narrative-output/analyst/
• Every finding in analyst.json has a code.script that points to an existing file

Analyst → Editor handoff check: If the Analyst finds no findings of "significance": "high", surface this to the user before proceeding - the dataset may be too sparse to support a meaningful narrative.

Role 3 - Editor

Load: ~/.copilot/skills/data-narrative/editor/SKILL.md

Task: Select a single narrative angle and draft the report.

Done when:

• narrative-output/editor.md exists (outline with chosen angle)
• narrative-output/report.md exists (full draft, ≤ 800 words body, no Provenance section yet)
• Every specific number in report.md appears verbatim in analyst.json or detective.json

Editor → Inspector handoff check: Cross-reference every statistic in report.md against analyst.json before handing off. If any number cannot be matched, re-task the Analyst to produce it before continuing.

Role 4 - Inspector

Load: ~/.copilot/skills/data-narrative/inspector/SKILL.md + ~/.copilot/skills/verification-before-completion/SKILL.md

Task: Bind every claim in report.md to its upstream evidence and write the provenance manifest.

Done when:

• narrative-output/inspector.json exists and is schema-valid
• ≥ 80% of quantitative claims in report.md must be bound to a Python script in narrative-output/analyst/ as their primary evidence source (evidence type: code). Claims sourced exclusively from detective.json may be tagged as evidence type reference and do not count toward the 80% threshold.
• report.md has the Provenance footer appended by the Inspector

Verification gate: After the Inspector writes inspector.json, confirm the file exists and contains a non-empty claims[] array. Report the claim count and evidence type distribution to the user.

Completion Report

After all four roles complete, present this summary to the user:

data-narrative pipeline complete
================================
Dataset        : <path>
Report         : narrative-output/report.md
Provenance     : narrative-output/inspector.json
Angle          : <chosen angle in one sentence>
Claims bound   : <n> total | <code_count> code evidence | <ref_count> reference evidence
Analyst scripts: <count> scripts in narrative-output/analyst/

To verify all claims:
  python ~/.copilot/skills/data-narrative/inspector/scripts/verify_claims.py \
      --inspector narrative-output/inspector.json

Output File Reference

Error Handling